Paper Citation Record · LEDGER

CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2310.17512.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2310.17512 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:32:35.023664Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-22T12:26:31.497738Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 0120ba64-f4e7-4446-add1-4025ecf6c6ac · inbound

Human Behavior Simulation: Objectives, Methodologies, and Open Problems cites this paper.

Human Behavior Simulation: Objectives, Methodologies, and Open Problems CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 204

Resolution: unresolved; no resolver link, observed 2026-08-12T12:32:35.023664Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f7e0fda-c46d-4cdc-8ab8-af094bd6217f · inbound

A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application cites this paper.

A Survey on LLM-based Multi-Agent System: Recent Advances and New Frontiers in Application CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-11T05:29:24.662106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9aa5f2e-0317-4328-bb20-25b4a395eaae · inbound

Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews cites this paper.

Agentic AI Systems Applied to tasks in Financial Services: Modeling and model risk management crews CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-08T19:24:00.139138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79ff551d-8b69-4055-b04b-1e7d08a382e5 · inbound

The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process cites this paper.

The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:15.679308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 36f8a441-8ba2-4b79-9923-3e8e7d822f62 · inbound

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching cites this paper.

MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T11:22:48.254823Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9921c208-c8a1-4c99-9b0a-421116ca1a5a · inbound

AI Agent Behavioral Science cites this paper.

AI Agent Behavioral Science CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 190

Resolution: unresolved; no resolver link, observed 2026-08-07T11:00:54.159647Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 346c9688-748b-4125-8cab-34d789917376 · inbound

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems cites this paper.

G-Memory: Tracing Hierarchical Memory for Multi-Agent Systems CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T05:39:58.864350Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e6272c5-e96d-4ee1-9a47-80b499b6220a · inbound

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study cites this paper.

Can Large Language Models Capture Human Risk Preferences? A Cross-Cultural Study CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T21:54:43.751412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ce6f650-8a93-4171-9bbd-8137397a106b · inbound

BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks cites this paper.

BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T23:41:54.550461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 31e8e7c9-550b-467a-b458-1ec863cb63c5 · inbound

Token-Level LLM Collaboration via FusionRoute cites this paper.

Token-Level LLM Collaboration via FusionRoute CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-22T12:26:31.501851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation bd7aed3d-0d4b-4b83-8f99-8e04c4ff332e · inbound

EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments cites this paper.

EconAI: Dynamic Persona Evolution and Memory-Aware Agents in Evolving Economic Environments CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-14T17:47:32.379888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f69a05c3-2062-4b10-96b2-fa820f0a9726 · inbound

AgentSociety 2: An Integrated Research Environment for Executable Social Science cites this paper.

AgentSociety 2: An Integrated Research Environment for Executable Social Science CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 111

Resolution: unresolved; no resolver link, observed 2026-08-02T11:43:51.009800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fdceb880-b61f-4f48-9ff8-cacc79e7e76e · inbound

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex cites this paper.

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex CompeteAI: Understanding the Competition Dynamics in Large Language Model-based Agents

Reference 197

Resolution: unresolved; no resolver link, observed 2026-07-31T23:52:06.598677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.